Esophageal atresia: predicting outcomes and decreasing mortality
Bibliographic record
Abstract
This literature review is devoted to the problem of predicting in-hospital mortality in newborns with esophageal atresia (EA). According to epidemiological study data, in developed countries, the mortality rate in newborns with EA ranges from 9% to 11% over the past 20 years. Three classifications were developed, Waterston 1962, Montreal 1993, and Spitz 1994, to assess the prognostic significance of risk factors. They considered birth weight, the presence of concomitant congenital malformations and pneumonia, and the need for mechanical ventilation. The choice of a model for predicting outcomes depends on the level of health care and other factors, such as prematurity, low birth weight, late diagnosis, and infectious complications. These factors have a greater impact on patient survival in developing countries than in developed ones, where insurmountable risk factors come out on top: combined congenital malformations and very low birth weight. Also, the magnitude of diastasis between segments of the esophagus creates difficulties in choosing surgical tactics and managing such patients in the postoperative period. In addition, the management of such patients in the intensive care unit, both preoperatively and postoperatively, has a significant impact on the outcome. The literature review underlined "pain points" in the treatment of newborns with EA in regions with different levels of medical care, the consideration of which will allow the achievement of better results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".